基于遗传算法的改进径向基支持向量机及其应用  

Improved RBF-SVM Based on Genetic Algorithm and Its Applications

作　　者：李良敏[1,2] 温广瑞[3] 王生昌[2] 

机构地区：[1]长安大学汽车运输安全保障技术交通行业重点实验室,西安710064 [2]长安大学汽车学院,西安710064 [3]西安交通大学智能仪器与监测诊断研究所,西安710049

出　　处：《系统仿真学报》2008年第22期6088-6092,6096,共6页Journal of System Simulation

基　　金：国家863发展计划(2006-AA04Z429)

摘　　要：通过对径向基核函数进行分析后发现:根据样本各个特征的识别能力赋予其不同大小的核参数,可以提高支持向量机的推广能力。此结论基础上,提出了一种基于遗传算法的多核参数径向基支持向量机算法,通过遗传算法最小化验证误差,实现了根据各个特征的识别能力赋予其不同大小的核参数。将该算法用于轴承故障诊断,实验结果表明,与传统支持向量机相比,多核参数径向基支持向量机具有更好的推广能力,同时,核参数的大小反映了对应特征识别能力的大小。The character of RBF kernel in support vector machine was discussed, and a conclusion was drawn that the generalization ability of support vector machine could be improved by giving larger kernel parameters to those features useless for the classification problem to lower their influence on kernel function. On the basis of this conclusion, an improved multi-kernel-parameter support vector machine with RBF kernel based on genetic algorithm was proposed, where genetic algorithm was applied to find optimum kernel parameters by minimizing validation error. Experiment results of rolling bearing fault diagnosis show that the improved multi-kernel-parameter support vector machine possesses better generalization ability than conventional support vector machine does, and the kernel parameters directly reflect the classification ability of corresponding features.

关 键 词：多核参数径向基支持向量机 遗传算法 核参数 验证误差 推广能力 故障诊断 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]